Artificial intelligence-enabled device for network connectivity independent delivery of consumable information

ABSTRACT

An artificial intelligence-enabled device that handles delivery of user consumable information independent of network connectivity of the AI-enabled device, includes a memory and neural circuitry. The neural circuitry allocates a dedicated cache storage and determines a type of intelligent service on the AI-enabled device, for which first information is to be cached at the dedicated cache storage. The neural circuitry caches first information from a cloud server to a local sub-cache in the dedicated cache storage. The first information of the determined type of service is adaptively cached during at least one of a background activity or a foreground activity of the AI-enabled device, in accordance with an offline state or an online state of the AI-enabled device. The neural circuitry further controls delivery of user consumable information, based on a user input, on the AI-enabled device, based on the local sub-cache and supplemental information retrievable from the cloud server.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

None.

FIELD

Various embodiments of the disclosure relate to on-device data cachingbased on machine learning technologies. More specifically, variousembodiments of the disclosure relate to an Artificial Intelligence(AI)-enabled device and method for delivery of user consumableinformation independent of network connectivity.

BACKGROUND

Recent advancements in AI technologies and different consumer electronicdevices have paved a way to access various cloud-enabled services ondifferent consumer electronic devices, for example, a television (TV).Also, recent developments in different technologies, such as speechrecognition, natural language processing, and machine learning, havemade it possible to use different applications that deliver differenttypes of actionable information (e.g., notifications) to users throughsmart phones, smart TVs, smart cars, and other devices. Typically, todeliver actionable information through different devices, suchapplications have to serve a request and retrieve data from differentcloud servers, every time the devices are connected to a network (e.g.,internet). The actionable information is usually present or derived fromthe data that is retrieved from different cloud servers. However, togenerate the actionable information or to extract the actionableinformation from the retrieved data, the devices have to be connected toan online network at the time of delivery of the actionable information.Consequently, there is a dependency to access cloud data for every userrequest, which in turn delays a time for delivery of the actionableinformation on such devices. For example, different services may clogthe network for a certain duration when the devices connect to theonline network or in certain scenarios, online connectivity may not beconsistently available. As a result, the response time, e.g., a totaltime between the time of reception of a user's request to the time ofdelivery of different actionable information, increases because theapplications have to wait for certain time required to download the datafrom the cloud server.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one skilled in the art, throughcomparison of described systems with some aspects of the presentdisclosure, as set forth in the remainder of the present application andwith reference to the drawings.

SUMMARY

An Artificial Intelligence (AI)-enabled device and method are providedfor delivery of user consumable information independent of a networkconnectivity, as shown in, and/or described in connection with, at leastone of the figures, as set forth more completely in the claims.

These and other features and advantages of the present disclosure may beappreciated from a review of the following detailed description of thepresent disclosure, along with the accompanying figures in which likereference numerals refer to like parts throughout.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a network environment, where user consumableinformation is delivered independent of network connectivity, inaccordance with an embodiment of the disclosure.

FIG. 2 illustrates a block diagram of an exemplary artificialintelligence (AI)-enabled device for delivery of user consumableinformation independent of network connectivity, in accordance with anembodiment of the disclosure.

FIG. 3 illustrates an exemplary scenario for implementation of theAI-enabled device of FIG. 2 for delivery of user consumable informationindependent of network connectivity, in accordance with an embodiment ofthe disclosure.

FIG. 4 illustrates an exemplary scenario of a local network environmentfor the AI-enabled device of FIG. 2 and a plurality of secondarydevices, where the AI-enabled device acts as an access point for theplurality of secondary devices, in accordance with an embodiment of thedisclosure.

FIGS. 5A to 5C, is a flowchart that collectively illustrates exemplaryoperations for delivery of user consumable information independent ofnetwork connectivity.

DETAILED DESCRIPTION

Certain embodiments of the disclosure may be found in an ArtificialIntelligence (AI)-enabled device that handles delivery of userconsumable information independent of network connectivity of theAI-enabled device (e.g., a smartphone, a smart TV, etc.). The AI-enableddevice may implement an AI learning model that is trained periodically(i.e. regularly) on user preferences, network connectivity informationof the AI-enabled device, usage information of intelligent services(e.g., a program recommendation service on a smart TV), and usageinformation of the AI-enabled device. The trained AI learning model maybe used to identify requirements of cloud data for a type of intelligentservice on the AI-enabled device. Further, the trained AI learning modelmay be used to adaptively cache cloud data from the cloud server, to alocal sub-cache in the dedicated cache storage of the AI-enabled device.In accordance with an embodiment, the AI-enabled device may adaptivelycache the cloud information during a background activity or a foregroundactivity of the AI-enabled device, in accordance with an offline stateor an online state of the AI-enabled device.

The AI-enabled device enables delivery of user consumable information,based on the adaptively cached cloud data in the local sub-cache, with amaximum dependency on the local sub-cache and a minimum dependency onthe supplemental information that may be retrieved from the cloud serverat the time of delivery of user-consumable information. In some cases,the user consumable information may be delivered with a maximumdependency on the local sub-cache and a zero dependency on thesupplemental information in the offline state (e.g., an offline networkconnection status on the AI-enabled device). The maximum dependency onthe local sub-cache may further minimize a response time between arequest to deliver the user consumable information and a delivery timeof the user consumable information at the AI-enabled device. Incontrast, conventional solutions deliver user consumable information ona conventional client device (e.g., a smart TV or a smartphone) bydownloading cloud data every time the conventional client deviceconnects to a network (e.g. internet). Different conventional services(e.g., maps, music, navigation, etc.) clog a network bandwidth that isavailable on the conventional device and therefore, the clogged networkbandwidth increases a response time between a user request and deliveryof user consumable information on the conventional client device.

FIG. 1 illustrates a network environment, where user consumableinformation is delivered independent of network connectivity, inaccordance with an embodiment of the disclosure. With reference to FIG.1, there is shown a network environment 100. The network environment 100includes an artificial intelligence (AI)-enabled device 102, a cloudserver 104, and a secondary device 106. The AI-enabled device 102 may becommunicatively coupled to the cloud server 104, via the communicationnetwork 108. There is further shown a local network 110. The AI-enableddevice 102 may be communicatively coupled to the secondary device 106,via the local network 110. There is further shown a user 112, who isassociated with the AI-enabled device 102 and the secondary device 106.

The AI-enabled device 102 may comprise suitable logic, circuitry, andinterfaces that may be configured to deliver user consumable informationindependent of network connectivity. The AI-enabled device 102 may cacheinformation associated with a type of service determined by theAI-enabled device 102. In accordance with an embodiment, the AI-enableddevice 102 may cache the information from the cloud server 104 to adedicated cache storage in the AI-enabled device 102. Examples of theAI-enabled device 102 may include, but are not limited to, televisions(e.g., smart TVs or ATSC TVs), digital media players, digital cameras,gaming consoles, smartphones, laptops, desktop computers, printers,smart speakers, and smart wearable electronics. In some embodiments, theAI-enabled device 102 may be a smart home appliance, such as a smartrefrigerator, a smart washing machine, and smart air conditioners. TheAI-enabled device 102 may communicate with other AI-enabled or non-AIdevices, such as the secondary device 106, via a communication network108.

The cloud server 104 may comprise suitable logic, circuitry, andinterfaces that may be configured to transmit supplemental information(e.g., a real time hyper local weather data for a personalized travelupdate on AI-enabled device 102) to the AI-enabled device 102, inresponse to requests received from the AI-enabled device 102. Therequest may be at least one of a user request, a device request, arequest automatically initiated by one or more intelligent services onthe AI-enabled device 102, or a request intermediated by a functionalservice on the cloud server 104 on behalf of the AI-enabled device 102.In accordance with an embodiment, the cloud server 104 may interactthrough functional services (not shown in the figure) to generate andtransmit additional data or service information, as requested, to theAI-enabled device 102. The type of service that is functional on theAI-enabled device 102 (or the type of information that is requested bythe AI-enabled device 102) may define a type of cloud service(functional service) offered by the cloud server 104. The cloud server104 may be configured to operate in a service-oriented architecture. Theservice-oriented architecture may define a service model, for example,an Infrastructure-as-a service (IAAS), a platform-as-a-service (PAAS), asoftware-as-a-service (SAAS), and the like.

The secondary device 106 may comprise suitable logic, circuitry, andinterfaces that may be configured to receive local sub-cache(s)distributed from the AI-enabled device 102, via the communicationnetwork 108 or the local network 110. The received local sub-cache maybe stored at a local storage on the secondary device 106. In accordancewith an embodiment, the secondary device 106 may receive a localtraining model (e.g., a trained, a partially trained, or an untrainedlearning model), generated at the AI-enabled device 102, from the localstorage of the AI-enabled device 102. The secondary device 106 may be apersonal device that is accessible to a user (e.g., the user 112) of theAI-enabled device 102. In certain cases, the secondary device 106 may bea device (e.g., a smart TV, a smart speaker, etc.) that is part of ahome network of devices. Such home network of devices may also includethe AI-enabled device 102 along with other devices, such as homesecurity devices, entertainment devices, and home automation devices(e.g., smart lights, smart switches, etc.). Examples of the secondarydevices 106 may include, but are not limited to, a smart phone, a smartTV, wearable's like a smart watch, Virtual Reality/AugmentedReality/Mixed Reality (AR/VR/MR) headsets, and smart speakers (enabledwith smart conversation agents).

The communication network 108 may comprise suitable logic, circuitry,and interfaces that may be configured to provide a plurality of networkports and a plurality of communication channels for transmission andreception of data. Each network port may correspond to a virtual address(or a physical machine address) for transmission and reception of thecommunication data. For example, the virtual address may be an InternetProtocol Version 4 (IPV4) (or an IPV6 address) and the physical addressmay be a Media Access Control (MAC) address. The communication network108 may include a medium through which the AI-enabled device 102, and/orthe cloud server 104 may communicate with each other. The communicationnetwork 108 may be associated with an application layer forimplementation of communication protocols based on one or morecommunication requests from at least one of the one or morecommunication devices. The communication data may be transmitted orreceived, via the communication protocols. Examples of such wired andwireless communication protocols may include, but are not limited to,Transmission Control Protocol and Internet Protocol (TCP/IP), UserDatagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), FileTransfer Protocol (FTP), ZigBee, EDGE, infrared (IR), IEEE 802.11,802.16, cellular communication protocols, and/or Bluetooth (BT)communication protocols.

Examples of the communication network 108 may include, but is notlimited to a wireless channel, a wired channel, a combination ofwireless and wired channel thereof. The wireless or wired channel may beassociated with a network standard which may be defined by one of aLocal Area Network (LAN), a Personal Area Network (PAN), a WirelessLocal Area Network (WLAN), a Wireless Sensor Network (WSN), WirelessArea Network (WAN), Wireless Wide Area Network (WWAN), a Long TermEvolution (LTE) network, a plain old telephone service (POTS), and aMetropolitan Area Network (MAN). Additionally, the wired channel may beselected on the basis of a bandwidth criteria. For example, an opticalfiber channel may be used for a high bandwidth communication. Further, acoaxial cable-based or Ethernet-based communication channel may be usedfor moderate bandwidth communication.

The local network 110 may include a medium through which the AI-enableddevice 102, and/or the secondary device 106 may communicate with eachother. The local network 110 may be a wired or wireless communicationnetwork. In accordance with an embodiment, the AI-enabled device 102 mayact as an access point for the secondary device 106, via the localnetwork 110. The local network 110 may include, but is not limited to, aLocal Area Network (LAN), a Wireless Local Area Network (WLAN), awireless home network (WHN), a wireless local area network (WLAN), or awireless ad hoc network (WANET), a 2^(nd) Generation (2G), a 3^(rd)Generation (3G), a 4^(th) Generation (4G) cellular, or a combinationthereof. Various devices in the network environment 100 may beconfigured to connect to the local network 110, in accordance withvarious wired and wireless communication protocols. Examples of suchwired and wireless communication protocols may include, but are notlimited to, TCP/IP, UDP, HTTP, File Transfer Protocol FTP, Zig Bee,EDGE, infrared (IR), IEEE 802.11, 802.16, cellular communicationprotocols, and/or Bluetooth (BT) communication protocols.

In operation, a plurality of intelligent services (e.g., an intelligenttravel suggestion service based on hyper local weather information and acalendar schedule of a user) may be operational on the AI-enabled device102. At least one of the plurality of intelligent services may utilizethe computational resources of the AI-enabled device 102, to generateand deliver consumable information (e.g., status updates, notifications,audio-video updates, personalized information etc.) to a user. The userconsumable information may be personalized information that is deliveredon the AI-enabled device 102, or other secondary devices associated withthe AI-enabled device 102. The user consumable information may include,but are not limited to, audio content, video content, text content,image, graphics, audio-visual notifications, actionable insights, userselectable options, guidance information, visual information,audio-visual recommendations, or a combination thereof.

Each intelligent service of the plurality of intelligent services may bea service handled by the AI-enabled device 102 using an AI learningmodel locally stored and updated at the AI-enabled device 102. Differentservices may be provided by one or more applications pre-installed inthe AI-enabled device 102. To provide different services, the one ormore applications may retrieve data from one or more cloud-basedresources, and generate certain output based on the processing of theretrieved data. Each intelligent service may be a service that interactswith the one or more applications to manage or modify data retrieval,caching, and other operations, using the local AI learning model. Inaccordance with an embodiment, each intelligent service of the pluralityof intelligent services may be a service that generates a new, aninferred, or an insightful output (not existing previously) using AIlearning model of the AI-enabled device 102 and sample data, such asuser preferences, network connectivity information of the AI-enableddevice 102, usage information of the plurality of intelligent servicesor the AI-enabled device 102. Examples of the plurality of intelligentservices may include, but are not limited to, an offline voice inputprocessing service or a personalized device control and recommendationservice.

In order to generate and deliver the consumable information, theAI-enabled device 102 may be configured to store instructions associatedwith the plurality of intelligent services that operates on theAI-enabled device 102. Also, the AI-enabled device 102 may be configuredto store training data to train a local AI learning model. The trainingdata may include a set of user preferences, network connectivityinformation of the AI-enabled device 102, and first usage information ofthe plurality of intelligent services. In certain cases, second usageinformation (e.g., device usage footprints, such as time interval forwhich the AI-enabled device 102 may be accessed by a user) of theAI-enabled device 102.

The set of user preferences may include historical preferences fordifferent services (e.g., music, TV, weather, maps, location, etc.)availed on the AI-enabled device 102 or the secondary device 106, andpreferences that are collected or inferred based on real time useractivities on the AI-enabled device 102. As an example, the set of userpreferences may include, but are not limited to, a type of music, agenre, a musician, a preferred artist, a preferred food type, a usertraveling time, a list of programs watched on the AI-enabled device 102or the secondary device 106. Such preferences may be collected,retrieved from logs (e.g., application logs, device activity logs, etc.)or inferred (or generated) based on different user activities and userinputs (e.g., user footprints based on textual inputs, searchedkeywords, voice inputs, touch inputs, etc.) on differentintelligent/non-intelligent applications on the AI-enabled device 102.An application may be classified as a non-intelligent application whenone or more operational components of the non-intelligent applicationexecute different instructions on the AI-enabled device 102, without adependence on an AI learning model locally stored and updated on theAI-enabled device 102. As an example, services on an AI-enabledsmartphone that don't have a need to cache information from the cloudserver 104 to deliver consumable information on the AI-enabled device102 or other secondary devices.

The network connectivity information of the AI-enabled device 102 mayinclude, but is not limited to, a bandwidth of the network (between theAI-enabled device 102 and the cloud server 104), a type of network, aduration for which the AI-enabled device 102 accesses the network. Thefirst usage information of the plurality of intelligent services mayinclude, but is not limited to, a type of information specified in auser request to the intelligent service, a time-based usage footprints,and a usage pattern of different functional services on the cloud server104 (e.g., how is the weather information or navigation information usedby a user during driving). Similarly, the second usage information ofthe AI-enabled device 102 may include, but are not limited to, a useractive duration on the AI-enabled device 102, usage logs of differenttypes of AI applications and non-AI applications on the AI-enableddevice 102, a usage pattern associated with the AI-enabled device 102(e.g., how a call may be made to interact with different APIs offunctional services at the cloud server 104).

Each intelligent service, operational on the AI-enabled device 102, maybe managed by a local application on the AI-enabled device 102, afunctional service on the cloud server 104 (e.g., a cloud serverdedicated for a type of intelligent service), or a combination thereof.A functional service may be a cloud service that manages therequirements (e.g. data required for an intelligent service) and one ormore operations of the intelligent services on the AI-enabled device102. In some cases, the intelligent service may be a self-managedservice that is operational on the AI-enabled device 102. Each of suchintelligent services may manage the local AI learning model on theAI-enabled device 102. The local AI learning model may be aservice-specific (or a service-independent) machine learning modeldeveloped, managed, trained, and utilized on the AI-enabled device 102(and/or the secondary device 106).

The local AI learning model may be generated by the AI-enabled device102 in a local storage (e.g., a dedicated memory) of the AI-enableddevice 102. The generation of the local AI learning model may correspondto the training of the local AI learning model on training data that maybe pre-stored and/or collected on the AI-enabled device 102, oradditional data retrieved from the cloud server 104. In someembodiments, the training of the local AI learning model may beaccelerated based on an AI accelerator circuitry (e.g., an AIaccelerator application-specific integrated circuit (ASIC)). The AIaccelerator circuitry may be an on-device (offline) AI acceleratorcircuitry (not shown in FIG. 1) or a server-end (online) AI acceleratorcircuitry (i.e., available on the cloud server 104). The learning rateand learning errors of the local AI learning model may be furtheroptimized based on specific learning optimization models, for example,heuristic or meta-heuristic optimization models, or non-heuristicmodels, such as Adagrad, Adadelta, Adamax, momentum, AMSgrad, etc.

The AI-enabled device 102 may include a cache storage that may storecache data for a certain type of intelligent service or different typesof intelligent services. The cached storage may be a dedicated cachestorage that may be allocated by the AI-enabled device 102 for one ormore of the plurality of intelligent services. The AI-enabled device 102may be further configured to determine a type of service (e.g., acalendar schedule service, a navigation service, a movie recommendationservice, etc.) associated with at least one of the plurality ofintelligent services, information for which is to be cached at thededicated cache storage. The type of service may be a service that isrequired to be accessed from the cloud-based resource to retrieve firstinformation (i.e. information that is to be cached at the dedicatedcache storage. For example, if location information from an area isrequired to be cached, then the type of service may be a map service.

The AI-enabled device 102 may be further configured to cache firstinformation (e.g., a TV program schedule, weather data, etc.),associated with the determined type of service from the cloud server104, to a local sub-cache in the dedicated cache storage the AI-enableddevice 102. In accordance with an embodiment, the first information maybe cached based on a mapping of the determined type of service to acorresponding functional service on the cloud server 104. In accordancewith an embodiment, the first information associated with the determinedtype of service may be adaptively cached during at least one of abackground activity or a foreground activity of the AI-enabled device102. Also, the first information may be cached in accordance with anoffline state or an online state of the AI-enabled device 102. Theonline state or the offline state may be specified in the networkconnectivity information stored on the AI-enabled device 102. The cachedfirst information may be downloaded and updated periodically from thecloud server 104, via the communication network 108. The online state ofthe AI-enabled device 102 may correspond to a network state where theAI-enabled device 102 may access the cloud server 104, via thecommunication network 108. The offline state of the AI-enabled device102 may correspond to a network state where the AI-enabled device 102may not be accessible to the cloud server 104, for example, a networkstate that indicates that the AI-enabled device 102 is disconnected froman internet access point.

The AI-enabled device 102 may be configured to control delivery of userconsumable information, associated with at least one of the plurality ofintelligent services, on the AI-enabled device 102. Such controlleddelivery of the user consumable information may be done based on atleast one of the local sub-cache and supplemental informationretrievable from the cloud server 104. The supplemental information mayinclude additional information that is required to be accessed in realtime or near real time for delivery of consumable information in a casewhere previously cached information may be insufficient or irrelevant togenerate the user consumable information. As an example, a smart travelstay recommendation service may need current route of the user's vehicleto generate and deliver travel stay recommendations. The user consumableinformation may be delivered with a maximum dependency on the localsub-cache and a minimum dependency on the supplemental information. Incertain embodiments, the user consumable information may be deliveredwith a zero dependency on the supplemental information. Alternativelystated, the AI-enabled device 102 may be configured to deliver the userconsumable information only based on data stored in the local sub-cacheor other on-device information on the AI-enabled device 102. In somecases, the maximum dependency on the generated local sub-cache mayfurther minimize a response time between a request to deliver the userconsumable information onto the AI-enabled device 102 and a deliverytime of the user consumable information onto the AI-enabled device 102.

In some embodiments, the user consumable information may be deliveredonto the AI-enabled device 102 based on a user input. The user input maybe received via an input device. The input device may be an embeddedinput device within the AI-enabled device 102, an externally coupledinput to the AI-enabled device 102 or the secondary device 106, or othernetwork interfaced or coupled input devices. Examples of the inputdevice may input device may include, but are not limited to, atouchscreen, a microphone, a keyboard, a mouse, a joystick, a hapticinput, a gesture input device, a motion sensor, a game controller, aremote (a non-TV remote), and a smart TV remote. In other embodiments,the user consumable information may be delivered based on a requestreceived from at least one of the AI-enabled device 102 or the cloudserver 104. The request may be at least one of a device request, arequest initiated by the at least one intelligent service, or afunctional service on the cloud server 104.

In some embodiments, the complexity of operations executed on theAI-enabled device 102 to deliver the user consumable information (e.g.,intelligent recommendations, notifications, etc.) may depend on variousfactors, such as a size of the training data, an operational complexityof an intelligent service, a computational complexity, a training timefor the local AI learning model, and the like. Therefore, in suchembodiments, some of the functional information, pre-trained models, orother service-oriented data may be pre-cached, from the cloud server104, on the AI-enabled device 102. The AI-enabled device 102 may beupdated with the user consumable information independent of networkconnectivity and within an optimal response time.

As shown in FIG. 1, the network environment 100 illustrates a specificnetwork architecture through which the user consumable information isdelivered onto the AI-enabled device 102. However, the specified networkarchitecture (in FIG. 1) may have different configurations and othernetwork components to achieve the same functional performance andoutput, without a deviation from the scope of present disclosure. Forexample, the delivery of user consumable information may be controlledby one AI-enabled device (e.g., the AI-enabled device 102) or adistributed network of AI-enabled devices. Also, instead of a singleAI-enabled device, there may be a plurality of AI-enabled devices and aplurality of the secondary devices engaged in communication with one ormore of a plurality of the cloud servers in the network environment 100.

In an exemplary embodiment, the AI-enabled device 102 may be a smart TVand the AI-enabled device 102 may extract information of recommendedvideos for a user (e.g., the user 112) based on the functional servicesused by the user (e.g., the user 112) in the past. In accordance with anembodiment, the AI-enabled device 102 may interact with functionalservices on the cloud server 104 via Application Program Interfaces(APIs) to perform functions related to intelligent services. Suchintelligent services may be, for example, switch on the TV, set an eventon a calendar application, make a telephone call, directions to adestination address, and the like. Such functions may be performed bythe AI-enabled device 102 after receiving the user input.

FIG. 2 illustrates a block diagram of an exemplary AI-enabled device fordelivery of user consumable information independent of networkconnectivity, in accordance with an embodiment of the disclosure. FIG. 2is explained in conjunction with elements from FIG. 1. With reference toFIG. 2, there is shown a block diagram 200 of the AI-enabled device 102.The AI-enabled device 102 may include one or more circuits, such as anetwork interface 202, an input/output (I/O) interface 204, a memory206. The memory 206 may include a user database 208, a dedicated cachestorage 210, and a local sub-cache 218 in the dedicated cache storage210. There is also shown neural circuitry 212 that may include aprocessor 214 and a learning circuit 216.

The network interface 202 may comprise suitable logic, circuitry, andinterfaces that may be configured to communicate with other systems anddevices in the network environment 100, via the communication network108 or and the local network 110. The network interface 202 may beimplemented by use of known technologies to support wired or wirelesscommunication of the AI-enabled device 102 with the communicationnetwork 108 and the local network 110. Components of the networkinterface 202 may include, but are not limited to, an antenna, a radiofrequency (RF) transceiver, one or more amplifiers, a tuner, one or moreoscillators, a digital signal processor, a coder-decoder (CODEC)chipset, a subscriber identity module (SIM) card, and/or a local buffercircuit.

The I/O interface 204 may comprise suitable logic, circuitry, andinterfaces that may be configured to operate as an I/O channel/interfacebetween a user (e.g., the user 112) and different operational componentsof the AI-enabled device 102 or other secondary devices (e.g., thesecondary device 106). The I/O interface 204 may facilitate an I/Odevice (for example, an I/O console) to receive an input from a user(e.g., the user 112) and present an output based on the received inputfrom the user. The I/O interface 204 may include various input andoutput ports to connect various I/O devices that may communicate withdifferent operational components of the AI-enabled device 102. Examplesof the input devices may include, but is not limited to, a touch screen,a keyboard, a mouse, a joystick, a microphone, and an image-capturedevice. Examples of the output devices may include, but is not limitedto, a display, a speaker, a haptic output device, or other sensoryoutput devices.

The memory 206 may comprise suitable logic, circuitry, and/or interfacesthat may be configured to store a machine code and/or instructionsexecutable by the neural circuitry 212 and the processor 214. The memory206 may include the user database 208 and the dedicated cache storage210. The dedicated cache storage 210 may further include the localsub-cache 218. The user database 208 may be configured to storeinstructions associated with a plurality of intelligent services thatoperates in the AI-enabled device 102, a set of user preferences,network connectivity information of the AI-enabled device 102, firstusage information of the plurality of intelligent services, and secondusage information of the AI-enabled device 102. The dedicated cachestorage 210 of the memory 206 may store first information, associatedwith a type of service, cached from the cloud server 104 to the localsub-cache 218. Examples of implementation of the memory 206 may include,but are not limited to, Random Access Memory (RAM), Read Only Memory(ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM),Hard Disk Drive (HDD), a Solid-State Drive (SSD), a CPU cache, and/or aSecure Digital (SD) card.

The neural circuitry 212 may comprise suitable logic, circuitry, andinterfaces that may be configured to handle operations of the pluralityof intelligent services on the AI-enabled device 102. The neuralcircuitry 212 may be configured to be trained on sample data, such asthe set of user preferences, the network connectivity information of theAI-enabled device 102, the first usage information of the plurality ofintelligent services, and the second usage information of the AI-enableddevice 102 stored in the user database 208. Further, the neuralcircuitry 212 may include multiple network layers, where each layerconstantly adjusts its weights while learning and fires thresholds untilthe output of its final layer consistently represents a solution for arequested intelligent service. The neural circuitry 212 may include theprocessor 214 and the learning circuit 216.

The processor 214 may comprise suitable logic, circuitry, and interfacesthat may be configured to execute instructions stored in the memory 206.Examples of the processor 214 may be an Application-Specific IntegratedCircuit (ASIC) processor, a Complex Instruction Set Computing (CISC)processor, a central processing unit (CPU), an Explicitly ParallelInstruction Computing (EPIC) processor, a Very Long Instruction Word(VLIW) processor, and/or other processors or circuits.

The learning circuit 216 may comprise suitable logic, circuitry, andinterfaces that may be configured to generate a local AI learning modelassociated with the AI-enabled device 102. In accordance with anembodiment, the local AI learning model may be generated based on theset of user preferences, the network connectivity information of theAI-enabled device 102, and the first usage information of the pluralityof intelligent services. The learning circuit 216 may be implementedbased on a number of processor technologies known in the art. Thelearning circuit 216 may be implemented based on a machine learningmodel, a deep learning model, such as a recurrent neural network (RNN),a convolutional neural network (CNN), and a feed-forward neural network,or a Bayesian model. The neural circuitry 212 may be communicativelycoupled to the network interface 202, the I/O interface 204, and thememory 206. The network interface 202 may communicate with the one ormore cloud servers, such as the cloud server 104, via the communicationnetwork 108 under the control of the neural circuitry 212.

In operation, a request may be received by the AI-enabled device 102,via the network interface 202. The request may be a user request, whichmay include user preferences, event information (e.g., weatherinformation), or a user input that may indicate a user preferenceassociated with a type of intelligent service (e.g., travel update basedon weather information) available on the AI-enabled device 102. In someembodiments, the request may be at least one of a device request, arequest initiated by an intelligent service, or a functional service onthe cloud server 104. For example, the user request may include a voiceinput, which may indicate a user request, such as “What is the weathertoday?” The voice input may be received through the I/O interface 204 ofthe AI-enabled device 102. The neural circuitry 212 may be configured todetermine the semantic expressions of the user (e.g., the user 112) fromvoice input and thereafter, the AI-enabled device 102 may call (an APIcall) a functional service of the cloud server 104 to interface with theweather forecast application on the AI-enabled device 102. Thefunctional service may be a cloud service that manages the requirementsand one or more operations of the intelligent services viacommunications with the intelligent services on the AI-enabled device102. As an example, a map server may have a functional service, such asa location API service, that manages location-related requirements ofthe intelligent service on the AI-enabled device 102.

The memory 206 may be configured to store, in the user database 208, theinstructions (associated with the plurality of intelligent services thatoperates in the AI-enabled device 102), a set of user preferences,network connectivity information of the AI-enabled device 102, the firstusage information of the plurality of intelligent services, and thesecond usage information of the AI-enabled device 102. The neuralcircuitry 212 may handle operations of the plurality of intelligentservices. The processor 214 of the neural circuitry 212 may allocate thededicated cache storage 210 for the plurality of intelligent services.

In accordance with an embodiment, the learning circuit 216 may beconfigured to train an adaptive machine learning model on the set ofuser preferences, the network connectivity information, the first usageinformation of the plurality of intelligent services, and the secondusage information of the AI-enabled device 102 stored in the userdatabase 208. The learning circuit 216 may be further configured togenerate a local AI learning model based on the trained adaptive machinelearning model. The local AI learning model may be generated furtherbased on the set of user preferences, the network connectivityinformation of the AI-enabled device 102, and the first usageinformation of the plurality of intelligent services. In certainembodiments, the local AI learning model is generated based on at leastone of a machine learning model, a deep learning model, or a Bayesianmodel. In accordance with an embodiment, the learning circuit 216 may befurther configured to update the local AI learning model based on a realtime or a near-real time change in user activity on the AI-enableddevice 102. The local AI learning model may be further updated based onvariations in network connectivity information (e.g., network bandwidth,available data rates, etc.), network connection status, device states(e.g., device usage logs), or other information associated with theAI-enabled device 102. In accordance with an embodiment, the set of userpreferences, the first usage information, and the second usageinformation may selectively change with a change in the user activity.

The processor 214 may be configured to determine a type of serviceassociated with at least one of the plurality of intelligent services,for which first information is to be cached at the dedicated cachestorage 210, based on the generated local AI learning model and thesecond usage information of the AI-enabled device 102. The processor 214may be further configured to cache the first information associated withthe determined type of service, from the cloud server 104, to the localsub-cache 218 in the dedicated cache storage 210 of the memory 206. Thefirst information associated with the determined type of service may beadaptively cached during at least one of a background activity or aforeground activity of the AI-enabled device 102. Also, the adaptivecaching may be done in accordance with an offline state or an onlinestate (specified in the network connectivity information) of theAI-enabled device 102. In accordance with an embodiment, the firstinformation may be cached based on a mapping of the determined type ofservice to a corresponding functional service on the cloud server 104.

For example, for an intelligent service that provides a suggestion for auser (e.g., the user 112) to switch “ON” a smart TV at a specific timeon a given day, the first information may include detailed programmingschedule of different programs that may be preferred by the user (e.g.,the user 112) and aired on the same day. Also, in some cases, the firstinformation may include graphics, tags, audio, or other informationassociated with the detailed programming schedule of different programs.The processor 214 may further implement the local AI learning model thatis previously trained on the user preferences, to generate anotification only on the basis of the first information in the localsub-cache 218 and other information stored on the AI-enabled device 102.

The processor 214 may be configured to generate user consumableinformation in accordance with a request received from a user, theAI-enabled device 102, or the cloud server 104. The user consumableinformation may be generated based on at least one of the generatedlocal sub-cache 218 or supplemental information retrievable from thecloud server 104 The supplemental information may include additionalinformation that is required to be accessed in real time or near realtime for delivery of consumable information in a case where previouslycached information may be insufficient or irrelevant to generate theuser consumable information. As an example, a smart travel stayrecommendation service may need current route of the user's vehicle togenerate and deliver travel stay recommendations. In accordance with anembodiment, the processor 214 may be configured to determine whether toretrieve the supplemental information from the cloud server 104 toupdate the local sub-cache 218 with a real time or a near real-timeinformation (associated with a type of service) on the cloud server 104.The decision may be executed by the processor 214 in accordance with thedetermined type of service and the updated local AI learning model. Insome embodiments, the decision to update the local sub-cache 218 may betaken, by the neural circuitry 212 or the cloud server 104, based onfurther changes in user activity or network status of the AI-enableddevice 102. The processor 214 may be further configured to determine arequirement for the supplemental information that is utilized to deliverthe user consumable information. In an event when one such requirementexists, the processor 214 may be configured to selectively retrieve thesupplemental information from the cloud server 104, via thecommunication network 108, based on the determined requirement for thesupplemental information in the online state. The processor 214 may beconfigured to update the first information in the local sub-cache 218with the retrieved supplemental information.

The user consumable information may be generated to assist a user (e.g.,the user 112) to take improved decisions, actions, interact withdifferent devices in vicinity of the AI-enabled device 102, automatedifferent tasks (e.g., switching a TV “ON” when a frequently watchedprogram is aired on a smart TV), and the like. Also, in some cases, theuser consumable information may be generated to alert a user (e.g., theuser 112) regarding an event, situation (e.g., a bad weather), or tocall for an action pending for the user. The user consumable informationmay include, but are not limited to, audio content, video content, textcontent, image, graphics, audio-visual notifications, actionableinsights, user selectable options, guidance information, visualinformation, audio-visual recommendations, or a combination thereof.

The processor 214 may be further configured to control delivery of theuser consumable information associated with at least one of theplurality of intelligent services of the AI-enabled device 102. Thedelivery of the user consumable information may be controlled based on auser input (e.g., a voice input). In some embodiments, the delivery ofthe user consumable information may be controlled based on a devicerequest from the AI-enabled device 102, a request from an intelligentservice on the AI-enabled device 102, or a request from a functionalservice on the cloud server 104. The delivery of the user consumableinformation may be based on at least one of the generated localsub-cache 218 or supplemental information retrievable from the cloudserver 104.

The user consumable information may be delivered with a maximumdependency on the local sub-cache 218 and a minimum dependency on thesupplemental information. For example, a voice input “What should I weartoday?” is received through microphones of the AI-enabled device 102(e.g., an AI-enabled smartphone). The neural circuitry 212 may receivethe voice input and further handle operations of an intelligent serviceto manage delivery of personalized apparel recommendations (i.e., theuser consumable information). The personalized apparel recommendationsmay be generated and/or delivered based on weather information,previously worn list of apparels, user preferences (e.g., for color,texture, day preferences, seasonal preferences, occasion or eventspecific preferences, etc.), and previous voice inputs that had similarcontext. The neural circuitry 212 may utilize a local AI learning modelthat is trained on different voice inputs from the user and understandsdifferent contexts, such as TV control inputs, apparel recommendationsrequests, and travel update requests from a user. Based on the local AIlearning model, the neural circuitry 212 may be configured to determinethat there is a requirement to cache images of black colored dressesstored on a cloud server. The requirement may be identified before thevoice input is received based on past activities of the user. Therefore,the neural circuitry 212 may be configure to adaptively cache images ofblack colored dresses at a time when the network connectivity isavailable and network bandwidth will be least affected from a cachingoperation. The neural circuitry 212 may further generate and deliver apersonalized apparel recommendation on the AI-enabled device 102 at atime when the voice input is received based on previously cached imagesof black colored dresses, and thereby the personalized apparelrecommendation is delivered without dependency on the networkconnectivity of the AI-enabled device 102. In some cases, currentweather information (i.e., the supplemental information) for a currentuser location may be required in real time to generate and deliver thepersonalized apparel recommendation. In such cases, the neural circuitrymay be configured to retrieve data, i.e., the current weatherinformation that minimally affects the network bandwidth as the data(i.e., the cached images of black colored dresses) that affects thenetwork bandwidth has been already cached on the AI-enabled device 102along with other information.

In certain embodiments, the user consumable information is deliveredwith a maximum dependency on the local sub-cache 218 and a zerodependency on the supplemental information in the offline state (e.g.,an offline network connection status on the AI-enabled device 102). Themaximum dependency on the local sub-cache 218 may further minimize aresponse time between a request to deliver the user consumableinformation and a delivery time of the user consumable information atthe AI-enabled device 102. In contrast, conventional solutions deliveruser consumable information on a conventional client device (e.g., asmart TV or a smartphone) by downloading cloud data every time theconventional client device connects to a network (e.g. internet).Different conventional services (e.g., maps, music, navigation, etc.)clog a network bandwidth that is available on the conventional deviceand therefore, the clogged network bandwidth increases a response timebetween a user request and delivery of user consumable information onthe AI-enabled device 102. Also, in some cases, as some of theconventional services rely only on the cloud data (i.e., that alsoincludes conventional user consumable information), in absence ofnetwork connectivity on the AI-enabled device 102, such conventionalservice may lack data resources to deliver user consumable informationon the AI-enabled device 102. The disclosed AI-enabled device 102 cachescloud data at times when network connection is available on theAI-enabled device 102 and delivers user consumable information, evenwhen network connection is not available.

In accordance with an embodiment, the processor 214 may be furtherconfigured to create a local network (e.g., the local network 110) amongthe AI-enabled device 102 and a plurality of secondary devices, such thesecondary device 106. The created local network may be at least one of awireless home network, a wireless local area network (WLAN), or awireless ad hoc network (WANET). In some embodiments, the local networkmay have the AI-enabled device 102 as an access point (e.g., a wirelessaccess point) for the plurality of secondary devices (e.g., thesecondary device 106). The processor 214 may be configured to distributethe local sub-cache 218, stored on the AI-enabled device 102, among theplurality of secondary devices, via the generated local network. Thelocal sub-cache 218 may be distributed in accordance with a devicespecification of each of the plurality of secondary devices. The devicespecification may indicate a capability of the plurality of secondarydevices to process and generate user consumable information based on thelocal sub-cache 218. The local sub-cache 218 may be distributed to alocal storage on the secondary device 106.

In some embodiments, the processor 214 may be further configured totransfer the generated local AI training model among the plurality ofsecondary devices, via the generated local network. The local AItraining model may be transferred to a local storage on the plurality ofsecondary devices, such as the secondary device 106. Also, in somecases, the processor 214 may be configured to deliver the userconsumable information, generated initially on the AI-enabled device102, to the plurality of secondary devices connected to each other viathe created local network. In accordance with an embodiment, the userconsumable information may be delivered based on the distributed localsub-cache 218 at the plurality of secondary devices and computationalresources of the plurality of secondary devices.

In some embodiments, the processor 214 may be further configured toidentify user-sensitive information and public information in the localsub-cache 218 (that may include the first information) on the AI-enableddevice 102. The processor 214 may be configured to assign a securityprivilege level to the identified user-sensitive information from thelocal sub-cache 218 for a type of intelligent service (e.g., a smart TVcontrol service). The security privilege level may be assigned based onpre-specified sensitivity information and a set of user-specifiedpermissions. For example, the local sub-cache 218 may include a user'slogin data, which may be set by the user (e.g., the user 112) (or basedon a service policy) as private data, i.e., user-sensitive information.Also, the local sub-cache 218 may include TV program schedule, which maybe pre-specified as public data by a publisher of TV content.

In accordance with an embodiment, the processor 214 may be furtherconfigured to share the identified user-sensitive information with asecondary device (e.g., the secondary device 106) via a local network(e.g., the local network 110). The identified user-sensitive serviceinformation may be shared based on authentication of the plurality ofsecondary devices for the assigned security level on the identifieduser-sensitive information. For example, if both the AI-enabled device102 and the secondary device 106 are shared by a common user, or acollective set of users (e.g., a family member that is registered on asmart TV (i.e., the AI-enabled device 102) and on a smartphone (i.e.,the secondary device 106). The user-sensitive information on the smartTV may be shared with the smartphone, via the local network, in a casewhere the smartphone is authenticated with the assigned security level.The detailed operation of the AI-enabled device 102 has been furtherdescribed, for example, in FIG. 3 and FIG. 4, respectively.

FIG. 3 illustrates an exemplary scenario for implementation of theAI-enabled device of FIG. 2 for delivery of user consumable informationindependent of network connectivity, in accordance with an embodiment ofthe disclosure. FIG. 3 is explained in conjunction with elements fromFIGS. 1 and 2. With reference to FIG. 3, there is shown a first scenario300 that depicts different stages of operation of the AI-enabled device102 for the delivery of user consumable information independent ofnetwork connectivity. The different states may include a pre-cachingstage 302, a caching stage 304, and a post-caching stage 306.

In the pre-caching stage 302, there is shown a service usage database308, a user preference database 310, an activity database 312, a deviceusage database 314, and a local AI learning model 316 stored in thememory 206 of an AI-enabled device, such as an AI-enabled smartphone318.

For example, the AI-enabled smartphone 318 may receive a user input thatmay be a request to update the user (e.g., the user 112) related to aweather forecast for next day. The AI-enabled smartphone 318 may displaya prompt for the user (e.g., the user 112) for a confirmation of therequest before calling an intelligent service. The AI-enabled smartphone318 may call (an API call) functional services of the cloud server 104to retrieve a weather forecast. The AI-enabled smartphone 318 may serveweather forecast information for the next day. The AI-enabled smartphone318 may concurrently also check various applications, for example, thecalendar application on the AI-enabled smartphone 318 and may find thatthe user (e.g., the user 112) has an appointment with doctor for thenext day. The AI-enabled smartphone 318 may cache the information in thelocal sub-cache 218 and may also remind the user (e.g., the user 112) tocarry an umbrella as it may rain and start early from his home locationas there may be congestion on road due to rain.

The request received, by the AI-enabled smartphone 318, may be at leastone of a user request, a device request, a request initiated by the atleast one intelligent service on the AI-enabled smartphone 318. Thememory 206 of the AI-enabled smartphone 318 may be configured to storeinstructions associated with a plurality of intelligent services thatoperates on the AI-enabled smartphone 318. Also, the memory 206 may befurther configured to collect and store a set of user preferences,network connectivity information of the AI-enabled smartphone 318, firstusage information of the plurality of intelligent services, and secondusage information of the AI-enabled smartphone 318.

In accordance with an embodiment, different instructions andinformations may be stored in different dedicated databases in thememory 206, present on the AI-enabled smartphone 318. The differentdatabases may include the service usage database 308, the userpreference database 310, the activity database 312, and the device usagedatabase 314. The service usage database 308 may include usage data ofdifferent intelligent and non-intelligent services on the AI-enabledsmartphone 318. As an example, the service usage database 308 mayinclude details of application programming interface (API) calls thatare made by different intelligent services (using the processor 214) tointeract with different APIs of functional services on the cloud server104. The user preference database 310 may include a daily or historicaluser preference related to different services (e.g., maps, navigations,location, television, calendar scheduling, etc.) offered on theAI-enabled smartphone 318, for example, music preferences, foodpreferences based on orders received through the AI-enabled smartphone318, and the like. The activity database 312 may include details ofuser's activity on the AI-enabled smartphone 318 and with other devices,for example, a smart TV, that are connected or share a common network(e.g., the local network 110) with the AI-enabled smartphone 318. Also,the activity database 312 may further include usage of differentfunctional services on the cloud server 104 or different intelligentservices on the AI-enabled smartphone 318. As an example, the usagedetails may include how the weather information or navigationinformation is used and accessed on the AI-enabled smartphone 318 whilethe user (e.g., the user 112) may be driving.

The device usage database 314 may include information of device usage,which may include, but are not limited to, device access logs, networkconnectivity information at different timestamps, usage duration ofdifferent device features (e.g., camera, internet, microphone, calling,etc.) along with a user access rate for different types of AIapplications and non-AI applications on the AI-enabled smartphone 318.The network connectivity information of the AI-enabled smartphone 318may include bandwidth of the network, type of network, networkconnection status (e.g., network connected/disconnected) at differenttime stamps, duration of the network connection and the like may bestored in the device usage database 314.

In the pre-caching stage 302, the local AI learning model 316 associatedwith the AI-enabled smartphone 318 may be generated based on the datafrom the service usage database 308, the user preference database 310,and the activity database 312, and the device usage database 314.Alternatively stated, the local AI learning model 316 may be trainedbased on the data from the service usage database 308, the userpreference database 310, the activity database 312, and the device usagedatabase 314. In accordance with an embodiment, the local AI learningmodel 316 may be further updated based on a real time or a near-realtime change in user activity on the AI-enabled smartphone 318. The datafrom the service usage database 308, the user preference database 310,the activity database 312, and the device usage database 314 mayselectively change with the change in the user activity.

In the pre-caching stage 302, the processor 214 may be configured todetermine a type of service (e.g., a weather forecast service)associated with at least one of the plurality of intelligent services(e.g., commute updates and routes based on weather forecastinformation), information for which is to be cached at the dedicatedcache storage 210. The type of service may be determined based on theapplication of the trained local AI learning model 316 on the datareceived from the service usage database 308, the user preferencedatabase 310, the activity database 312, and the device usage database314.

For example, for a commute update service on the AI-enabled smartphone318, the processor 214 may determine services, e.g., a weather serviceand a navigation service, for which more information is required fromthe cloud server 104. The processor 214 determine a requirement ofweather data for a region that is hyper local to the user (e.g., theuser 112) of the AI-enabled smartphone 318 and a daily commute routethat is taken by the user (e.g., the user 112) from office to home. Insome embodiments, the requirement may be determined based on the trainedlocal AI learning model 316 on data received from the service usagedatabase 308, the user preference database 310, the activity database312, and the device usage database 314. Also, in some cases, therequirement may be further determined based on the request (that mayinclude request data, such as a user voice input) received from theuser, an intelligent service on the AI-enabled smartphone 318, or afunctional service on the cloud server 104.

In the caching stage 304, there is shown a cloud server (i.e., the cloudserver 104) and the dedicated cache storage 210 present in the memory206 of the AI-enabled smartphone 318. The dedicated cache storage 210may be allocated, by the processor 214, in the memory 206 of theAI-enabled smartphone 318. The dedicated cache storage 210 may beallocated for a type of service associated with the plurality ofintelligent services. The first information associated with thedetermined type of service may be cached from the cloud server 104 to alocal sub-cache (shown in the caching stage 304) in the dedicated cachestorage 210 of the memory 206 of the AI-enabled smartphone 318. Thefirst information of the determined type of service may be adaptivelycached during at least one of a background activity or a foregroundactivity of the AI-enabled smartphone 318. Also, the first informationmay be cached in accordance with an offline state or an online state(specified in the network connectivity information) of the AI-enabledsmartphone 318. Alternatively stated, the first information may becached in a period when the AI-enabled smartphone 318 is connected to anetwork (which allows connectivity to the cloud server 104) and animpact of caching of the first information on the network bandwidthavailable to the AI-enabled smartphone 318 is minimal (or optimal). Theadaptability may be implemented by intelligently monitoring status ofthe network and user activity on the AI-enabled smartphone 318. Forexample, if it is known from device logs that a user “X” access achatting service on the AI-enabled smartphone 318, between 9:00 AM to10:30 AM, then the caching of the first information may be scheduledafter 10:30 AM for user consumable information that may have to bedelivered after 10:30 AM.

In accordance with an embodiment, the cached first information may bedownloaded from the cloud server 104, via the communication network 108,for example, via an internet connection and updated, at specific timeintervals as a background service on the AI-enabled smartphone 318. Forexample, the user (e.g., the user 112) may ask for the news headlinedaily, the AI-enabled smartphone 318 may not cache first informationassociated with news information from the cloud server 104 every timethe AI-enabled smartphone 318 connects to a network (e.g., an internetnetwork). Instead, the news information may be cached at a specific timeof day when the AI-enabled smartphone 318 may witness low networkactivity or a device activity. The first information may be furthercached in the local sub-cache 218 based on user's profile, userpreferences, or use cases inferred from daily AI learnings on theAI-enabled smartphone 318.

In the caching stage 304, the processor 214 may be further configured todetermine whether to retrieve supplemental information from the cloudserver 104, to update the local sub-cache 218 with real time or nearreal-time updates (associated with a type of service) in accordance tothe determined type of service and the updated AI learning model. Inaccordance with an embodiment, the AI-enabled smartphone 318 may befurther configured to selectively retrieve the supplemental informationfrom the cloud server 104, via the communication network 108, based onthe determined requirement for the supplemental information from thecloud server 104 in the online state. In accordance with an embodiment,the AI-enabled smartphone 318 may be further configured to update thefirst information in the local sub-cache with the retrieved supplementalinformation.

In accordance with an embodiment, the neural circuitry 212 in theAI-enabled smartphone 318 may decide whether to apply the cached firstinformation in local sub-cache or pull new information/update from thecloud server 104, to update the first information in the local sub-cache218 of the dedicated cache storage 210 of the memory 206, in theAI-enabled smartphone 318.

In the post-caching stage 306, there is shown a display screen of theAI-enabled smartphone 318. The post-caching stage 306 may be done afterthe first information has been cached in the local sub-cache 218 of thededicated cache storage 210. The processor 214 on the AI-enabledsmartphone 318 may control a delivery of user consumable information(associated with at least one of the plurality of intelligent services)on the AI-enabled smartphone 318. The controlled delivery of the userconsumable information may be done based on at least one of the localsub-cache 218 or the supplemental information retrievable from the cloudserver 104. As an example, as shown in the post-caching stage 306, theuser consumable information may include a set of intelligentnotifications 320 that may be generated and delivered, by the neuralcircuitry 212, on the AI-enabled smartphone 318, based on the localsub-cache 218 in the memory 206. For example, the set of intelligentnotifications 320 may indicate a “call to action” for a specific type ofintelligent service, an actionable insight as per user's preferences, analert message for an event or situation associated with the user.

FIG. 4 illustrates an exemplary scenario of a local network environmentbetween the AI-enabled device of FIG. 2 and a secondary device, wherethe AI-enabled device acts as an access point for the secondary device,in accordance with an embodiment of the disclosure. FIG. 4 is explainedin conjunction with elements from FIGS. 1, 2, and 3. With reference toFIG. 4, there is shown a second scenario 400, where a smart TV 402, asmartphone 404, and a smart speaker 406 are communicatively coupled toeach other via a local network 408.

The smart TV 402 may be an AI-enabled smart TV. There is further shown alocal sub-cache 410 in the smart TV 402. In some embodiments, the localsub-cache 410 may include different data fragments, based on a pluralityof intelligent services associated with the smart TV 402. In otherwords, different intelligent services may have a separate data fragmentin the local sub-cache 410 or may have a dedicated local sub-cache in acache storage in the smart TV 402.

In accordance with an embodiment, the intelligent services of thesmartphone 404 and the smart speaker 406 may need to connect to thecloud server 104 for a delivery of user consumable information. However,at times, the network connectivity between the secondary devices likethe smartphone 404 and the cloud server 104 may be slow, absent or maynot be reliable in certain situations. For example, some AI services mayrequire access to or integration with end users current data. When suchdata may be sensitive (e.g. personal information), integrating them witha remote service, like the cloud server 104 may be risky, or mayconflict with data regulations. Therefore, for user sensitive data, thelocal network 408 may be generated at an AI-enabled device, like thesmart TV 402. The intelligent services at the smartphone 404 may managethe local sub-cache 410, via the local network 408, with or withoutinternet access in real time or near real-time. For example, for asimple operation like switch on the smart TV 402 using a voice inputfrom user, there is no need to interact with the cloud server 104. Thesmart TV 402 may itself process the voice input based on cache data inthe local sub-cache 410 and switch on the smart TV 402.

In accordance with an embodiment, the smart TV 402 may be furtherconfigured to create the local network 408 to connect with a pluralityof secondary devices, i.e., the smartphone 404 and the smart speaker406. The smart TV 402 may be further configured to distribute the localsub-cache 410 between the smartphone 404 and the smart speaker 406, viathe generated local network 408. The local sub-cache 410 may bedistributed in accordance with a device specification that may indicatecapability of the smartphone 404 and the smart speaker 406 to processapplication-specific information from the distributed local sub-cache410. The local sub-cache 410 may be distributed to a local storage onthe smartphone 404 and the smart speaker 406, respectively. Thesmartphone 404 may include a fragment 412 of the local sub-cache 410 andthe smart speaker 406 may include a fragment 414 of the local sub-cache410. The local sub-cache 410 may be distributed in order to allowsecondary devices that are not connected to network (e.g., internet) todeliver consumable information on the smartphone 404 and the smartspeaker 406 (i.e., the secondary device 106). The local sub-cache 410may be further distributed in order to allow secondary devices that mayneed information specific to the AI-enabled device 102 to deliverconsumable information on the smartphone 404 and the smart speaker 406(i.e., the secondary device 106). Also, the local sub-cache 410 may befurther distributed in order to save time and bandwidth for cachingoperation on the secondary devices when the local sub-cache 410 isalready present on the smart TV 402. For example, in order to recommenda time to switch on the smart TV 402 to view a program, the smartspeaker 406 may need the local sub-cache 410 (or a fragment of the localsub-cache 410) that includes details of different schedules and otherepisode information for the program that is daily watched on the smartTV 402.

The smart TV 402 may be further configured to transfer a local AIlearning model (generated on the smart TV 402) to the smartphone 404 andthe smart speaker 406, via the generated local network 408. Inaccordance with an embodiment, the local AI learning model may betransferred when the secondary devices (i.e., the smartphone 404 and thesmart speaker 406) lack the capacity to generate and train a localAI-learning model or when the transfer of the local AI learning modelmay save re-training and development time for the secondary devices.More specifically, the local AI training model may be transferred to thelocal storage on the smartphone 404 and to the local storage on thesmart speaker 406. The transferred local AI learning model may bepreviously trained on data (as described in FIGS. 1, 2, and 3) storedand collected in real time on the smart TV 402. In some embodiments, thesmartphone 404 and the smart speaker 406 may only receive a portion ofthe local AI learning model, which is trained on data that is specificto the intelligent services that can be availed via the smartphone 404and the smart speaker 406. For example, the smart speaker 406 mayreceive a portion of the local AI learning model that is trained on thevoice inputs from a user (e.g., the user 112) and intelligent services(e.g., voice-based selection of a programming channel, on-air program,or an on-demand program on the smart TV 402) that may require amicrophone, a speaker, or a conversational agent on the smart TV 402.

The smart TV 402 may be further configured to deliver user consumableinformation to the smartphone 404 and the smart speaker 406 connected toeach other via the generated local network 408. The user consumableinformation may be delivered based on computational resources of thesmartphone 404 and the smart speaker 406. Also, the user consumableinformation may be delivered based on accessibility of the smartphone404 and the smart speaker 406 to the user. The computational resourcesmay indicate a capability of the smartphone 404 and the smart speaker406 to output the user consumable information in a certain manner thatis possible for the smartphone 404 and the smart speaker 406.Alternatively stated, the capability represents a configured means(e.g., a speech output for the smart speaker) through which the userconsumable information is generated and/or delivered by the smartphone404 and the smart speaker 406. The smart speaker 406 may only receivevoice-based user consumable information from the smart TV 402, fordelivery to a user. The smart TV 402 may not deliver visualnotifications (i.e. user consumable information) to the smart speaker406 as visual display may be absent from smart speaker 406. For example,the smart speaker 406 may output the user consumable information throughvoice instructions or conversations with a user. Similarly, thesmartphone 404 may be currently accessible to the user, instead of thesmart TV 402. Therefore, the smartphone 404 may be selected to outputthe user consumable information instead of the smart TV 402.

FIGS. 5A to 5C, collectively illustrate a flowchart that depictsexemplary operations for delivery of user consumable informationindependent of network connectivity, in accordance with an embodiment ofthe disclosure. FIG. 5 is explained in conjunction with elements fromFIGS. 1, 2, 3, and 4. With reference to FIG. 5, there is shown aflowchart 500. The method, in accordance with the flowchart 500, may beimplemented on the AI-enabled device 102. The method starts at 502 andproceeds to 504.

At 504, instructions associated with the plurality of intelligentservices that operates on AI-enabled device 102, a set of userpreferences, network connectivity information of the AI-enabled device102, first usage information of the plurality of intelligent services,and second usage information of the AI-enabled device 102 may be stored.The memory 206 may be configured to store the instructions associatedwith the plurality of intelligent services. Also, the memory 206 may beconfigured to store the set of user preferences, the networkconnectivity information of the AI-enabled device 102, the first usageinformation of the plurality of intelligent services, and the secondusage information of the AI-enabled device 102.

At 506, a request may be received from AI-enabled device 102 or thecloud server 104 to deliver user consumable information. The neuralcircuitry 212 may be configured to receive the request from AI-enableddevice 102 or the cloud server 104, to deliver user consumableinformation.

At 508, the dedicated cache storage 210 for plurality of intelligentservices may be allocated in the memory 206. The neural circuitry 212may be configured to allocate the dedicated cache storage 210 for theplurality of intelligent services in the memory 206 on the AI-enableddevice 102.

At 510, an adaptive machine learning model may be trained on the set ofuser preferences, the network connectivity information of the AI-enableddevice 102, the first usage information of the plurality of intelligentservices, and the second usage information of the AI-enabled device 102.The neural circuitry 212 may be configured to train an adaptive machinelearning model on the set of user preferences, the network connectivityinformation of the AI-enabled device 102, the first usage information ofthe plurality of intelligent services, and the second usage informationof the AI-enabled device 102.

At 512, a local AI learning model may be generated associated withAI-enabled device 102. Such generation of the local AI learning modelmay be done based on the set of user preferences, the networkconnectivity information of the AI-enabled device 102, and the firstusage information of the plurality of intelligent services. The learningcircuit 216 of the neural circuitry 212 may be configured to generatethe local AI learning model (associated with AI-enabled device 102) onthe AI-enabled device 102.

At 514, the local AI learning model may be updated based on real time ornear-real time change in a user activity on the AI-enabled device 102.The learning circuit 216 of the neural circuitry 212 may be configuredto update the local AI learning model based on real time or near realtime change in the user activity on the AI-enabled device 102. The setof user preferences, the first usage information, and the second usageinformation may selectively change with a change in the user activity.

At 516, a type of service associated with at least one of plurality ofintelligent services may be determined, for which first information isto be cached at the dedicated cache storage 210. Such determination ofthe type of service may be done based on the generated local AI learningmodel and the second usage information of the AI-enabled device 102. Theneural circuitry 212 may be configured to determine a type of service,for which first information is to be cached at the dedicated cachestorage 210.

At 518, the first information, associated with determined type ofservice may be cached from the cloud server 104, to the local sub-cache218 in the dedicated cache storage 210 of the memory 206. The neuralcircuitry 212 may be configured to cache the first information,associated with determined type of service, from the cloud server 104,to the local sub-cache 218 in the dedicated cache storage 210 of thememory 206.

At 520, it may be determined whether a retrieval of the supplementalinformation is required from the cloud server 104. The neural circuitry212 may be configured to determine whether the retrieval of thesupplemental information is required from the cloud server 104. In acase where the requirement exists, control passes to 522. Otherwise,control passes to 524.

At 522, the supplemental information may be retrieved and updated in thelocal sub-cache 218. The neural circuitry 212 may be configured toretrieve and update the supplemental information in the local sub-cache218.

At 524, delivery of user consumable information, associated with one ofthe plurality of intelligent services of AI-enabled device 102, may becontrolled based on a user input. The neural circuitry 212 may beconfigured to control, based on the user input, delivery of userconsumable information, associated with one of the plurality ofintelligent services, on the AI-enabled device 102.

At 526, it may be determined whether a requirement to share at least oneof the local AI learning model or the first information in the localsub-cache 218 exists. The neural circuitry 212 (or a functional serviceon the cloud server 104) may be configured to determine whether therequirement to share the local AI learning model or the firstinformation in the local sub-cache 218 exists. In a case where therequirement exists, control passes to 528. Otherwise, control passes toend.

At 528, a local network (e.g., the local network 110) may be generatedbetween the AI-enabled device 102 and the secondary device 106. Theneural circuitry 212 may be configured to generate the local networkbetween the AI-enabled device 102 and the secondary device 106.

At 530, the local sub-cache 218 may be distributed among a plurality ofsecondary devices via the generated local network 110, in accordancewith a device specification that indicates capability of plurality ofthe secondary devices. The neural circuitry 212 may be configured todistribute the local sub-cache 218 among the plurality of secondarydevices via the generated local network 110, in accordance with a devicespecification that indicates capability of the plurality of thesecondary devices.

At 532, the generated local AI training model may be transferred to theplurality of secondary devices, via the generated local network 110. Theneural circuitry 212 may be configured to transfer the generated localAI training model to the plurality of secondary devices, via thegenerated local network 110.

At 534, the user consumable information may be transferred to one ormore secondary devices of the plurality of secondary devices connectedto each other via the generated local network 110. The neural circuitry212 may be configured to transfer the user consumable information to theone or more secondary devices connected to each other via the generatedlocal network 110.

At 536, it may be determined whether the generated local sub-cache 218includes user-sensitive information. The neural circuitry 212 may beconfigured to determine whether the generated local sub-cache 218includes user-sensitive information. In a case where user-sensitiveinformation is included in the generated local sub-cache 218, controlpasses to 538. Otherwise, control passes to end.

At 538, the user-sensitive information and public information may beidentified in the first information stored in generated local sub-cache218. The neural circuitry 212 may be configured to identify theuser-sensitive information and public information in the firstinformation, stored in generated local sub-cache 218.

At 540, a security privilege level may be assigned to the firstinformation, based on a pre-specified sensitivity information and a setof user-specified permissions to identify user-sensitive informationfrom the generated local sub-cache 218. The neural circuitry 212 may beconfigured to assign a security privilege level to the firstinformation, based on a pre-specified sensitivity information and a setof user-specified permissions. The security privilege level may beassigned to identify the user-sensitive information in the generatedlocal sub-cache 218.

At 542, the identified user-sensitive information may be shared with theplurality of secondary devices connected to the AI-enabled device 102,via the generated local network 110. The neural circuitry 212 may beconfigured to share the identified user-sensitive information with theplurality of secondary devices connected to the AI-enabled device 102,via the generated local network 110. The identified user-sensitiveservice information may be shared based on authentication of theplurality of secondary devices for the assigned security level. Controlpasses to end.

Various embodiments of the disclosure may provide a non-transitorycomputer readable medium and/or storage medium, and/or a non-transitorymachine readable medium and/or storage medium with a machine code and/ora set of instructions stored thereon and executable by a machine and/ora computer to provide user consumable information independent of networkconnectivity. The set of instructions in the AI-enabled device 102 maycause the machine and/or computer to store instructions associated witha plurality of intelligent services that operates in the AI-enableddevice 102. The neural circuitry may be configured to allocate adedicated cache storage for the plurality of intelligent services,generate a local AI learning model associated with the AI-enableddevice, and determine a type of service associated with at least one ofthe plurality of intelligent services. The neural circuitry may beconfigured to cache first information associated with the determinedtype of service from a cloud server to a local sub-cache in thededicated cache storage of the memory. The neural circuitry may beconfigured to control, based on a user input, delivery of userconsumable information associated with at least one of the plurality ofintelligent services of the AI-enabled device.

Various embodiments of the present disclosure may be found in a methodand an AI-enabled device (e.g., the AI-enabled device 102) that handlesdelivery of user consumable information independent of networkconnectivity of the AI-enabled device. The AI-enabled device may includea memory (e.g., the memory 206) and neural circuitry (e.g., the neuralcircuitry 212). The memory may be configured to store instructionsassociated with a plurality of intelligent services that operates in theAI-enabled device, a set of user preferences, network connectivityinformation of the AI-enabled device, first usage information of theplurality of intelligent services, and second usage information of theAI-enabled device. The neural circuitry may handle operations of theplurality of intelligent services on the AI-enabled device. The neuralcircuitry may be configured to allocate a dedicated cache storage (e.g.,the dedicated cache storage 210) for the plurality of intelligentservices and generate a local AI learning model associated with theAI-enabled device. The local AI learning model may be generated based onthe set of user preferences, the network connectivity information of theAI-enabled device, and the first usage information of the plurality ofintelligent services. The neural circuitry may be further configured todetermine a type of service associated with at least one of theplurality of intelligent services, first information for which is to becached at the dedicated cache storage. The type of service may bedetermined based on the generated local AI learning model and the secondusage information of the AI-enabled device. The neural circuitry may befurther configured to cache the first information, associated with thedetermined type of service, from a cloud server (e.g., the cloud server104) to a local sub-cache (e.g., the local sub-cache 218) in thededicated cache storage of the memory. The first information of thedetermined type of service may be adaptively cached during at least oneof a background activity or a foreground activity of the AI-enableddevice. Also, the first information may be cached in accordance with anoffline state or an online state, specified in the network connectivityinformation, of the AI-enabled device. The first information may befurther cached based on a mapping of the determined type of service to acorresponding functional service on the cloud server. The neuralcircuitry may be further configured to control, based on a user input,delivery of user consumable information, associated with at least one ofthe plurality of intelligent services, on the AI-enabled device. Thedelivery may be controlled further based on at least one of thegenerated local sub-cache and supplemental information retrievable fromthe cloud server.

The user consumable information may be delivered with a maximumdependency on the local sub-cache and a minimum dependency on thesupplemental information. In some embodiments, the user consumableinformation may be delivered further based on a request received from atleast one of the AI-enabled device and the cloud server. The request maybe at least one of a user request, a device request, a request initiatedby the at least one intelligent service, or the functional service onthe cloud server. As an example, the user consumable informationassociated with the plurality of intelligent services may include atleast one of audio content, video content, text content, image,graphics, and audio-visual notifications. The maximum dependency on thegenerated local sub-cache may further minimize a response time between arequest to deliver the user consumable information and a delivery timeof the user consumable information at the AI-enabled device. In certainembodiments, the user consumable information is delivered with a maximumdependency on the local sub-cache and a zero dependency on thesupplemental information, in the offline state of the AI-enabled device.

In accordance with an embodiment, the neural circuitry may be furtherconfigured to train an adaptive machine learning model on the set ofuser preferences, the network connectivity information of the AI-enableddevice, the first usage information of the plurality of intelligentservices, and the second usage information of the AI-enabled device. Thelocal AI learning model may be generated further based on the trainedadaptive machine learning model. The local AI learning model may begenerated based on at least one of a machine learning model, a deeplearning model, and a Bayesian model. The neural circuitry may befurther configured to update the local AI learning model based on a realtime or a near-real time change in a user activity on the AI-enableddevice. The set of user preferences, the first usage information, andthe second usage information may selectively change with the change inthe user activity.

In accordance with an embodiment, the neural circuitry may be furtherconfigured to determine whether to retrieve the supplemental informationfrom on the cloud server, to update the supplemental information in thegenerated local sub-cache. Also, the neural circuitry may be configuredto determine a requirement for the supplemental information that isutilized to deliver the user consumable information. Thereafter, theneural circuitry may be further configured to selectively retrieve thesupplemental information from the cloud server, via a communicationnetwork, based on the determined requirement for the supplementalinformation in the online state. The first information may be updated inthe generated local sub-cache with the retrieved supplementalinformation.

In accordance with an embodiment, the neural circuitry may be furtherconfigured to generate a local network (e.g., the local network 110)among the AI-enabled device and a plurality of secondary devices (e.g.,the secondary device 106). The generated local network may be at leastone of a wireless home network, a wireless local area network, or awireless ad hoc network. The AI-enabled device may act as an accesspoint for the plurality of secondary devices. Thereafter, the neuralcircuitry may be further configured to distribute the generated localsub-cache in the AI-enabled device among the plurality of secondarydevices, via the generated local network. The generated local cache maybe distributed in accordance with a device specification that indicatescapability of the plurality of secondary devices. More specifically, thegenerated local sub-cache may be distributed to a local storage on theplurality of secondary devices.

In accordance with an embodiment, the neural circuitry may be furtherconfigured to transfer the generated local AI training model among theplurality of secondary devices, via the generated local network. Thelocal AI training model may be transferred to a local storage on theplurality of secondary devices. Also, in some cases, the neuralcircuitry may be further configured to deliver the user consumableinformation to one or more secondary devices of the plurality ofsecondary devices, connected to each other via the generated localnetwork. The user consumable information may be delivered, based on thedistributed local sub-cache on the plurality of secondary devices andcomputational resources of the plurality of secondary devices.

In accordance with an embodiment, the neural circuitry may be furtherconfigured to identify the generated local sub-cache that includes thefirst information, into user-sensitive information and publicinformation. The neural circuitry may be further configured to assign asecurity privilege level to the identified user-sensitive informationfrom the generated local sub-cache of the type of service. The securityprivilege level may be assigned based on pre-specified sensitivityinformation and a set of user-specified permissions. In someembodiments, the neural circuitry may be further configured to share theidentified user-sensitive information with the plurality of secondarydevices connected, via a local network, with the AI-enabled device. Theidentified user-sensitive service information may be shared based onauthentication of the plurality of secondary devices for the assignedsecurity level on the identified user-sensitive information.

The present disclosure may be realized in hardware, or a combination ofhardware and software. The present disclosure may be realized in acentralized fashion, in at least one computer system, or in adistributed fashion, where different elements may be spread acrossseveral interconnected computer systems. A computer system or otherapparatus adapted to carry out the methods described herein may besuited. A combination of hardware and software may be a general-purposecomputer system with a computer program that, when loaded and executed,may control the computer system such that it carries out the methodsdescribed herein. The present disclosure may be realized in hardwarethat comprises a portion of an integrated circuit that also performsother functions.

The present disclosure may also be embedded in a computer programproduct, which comprises all the features that enable the implementationof the methods described herein, and which when loaded in a computersystem is able to carry out these methods. Computer program, in thepresent context, means any expression, in any language, code ornotation, of a set of instructions intended to cause a system that hasan information processing capability to perform a particular functioneither directly, or after either or both of the following: a) conversionto another language, code or notation; b) reproduction in a differentmaterial form.

While the present disclosure has been described with reference tocertain embodiments, it will be understood by those skilled in the artthat various changes may be made and equivalents may be substitutedwithout departure from the scope of the present disclosure. In addition,many modifications may be made to adapt a particular situation ormaterial to the teachings of the present disclosure without departurefrom its scope. Therefore, it is intended that the present disclosurenot be limited to the particular embodiment disclosed, but that thepresent disclosure will include all embodiments that falls within thescope of the appended claims.

What is claimed is:
 1. An artificial intelligence (Al)-enabled device,comprising: a memory configured to store instructions associated with aplurality of intelligent services that operates in the Al-enableddevice, a set of user preferences, network connectivity information ofthe Al-enabled device, first usage information of the plurality ofintelligent services, and second usage information of the Al-enableddevice; and neural circuitry that handles operations of the plurality ofintelligent services on the Al-enabled device, wherein the neuralcircuitry is configured to: allocate a dedicated cache storage for theplurality of intelligent services; generate a local Al learning modelassociated with the Al-enabled device, wherein the local Al learningmodel is generated based on the set of user preferences, the networkconnectivity information of the Al-enabled device, and the first usageinformation of the plurality of intelligent services; determine a typeof service and first information, wherein the type of service isassociated with at least one of the plurality of intelligent services,the first information is associated with the determined type of service,the first information cached at the dedicated cache storage, based onthe generated local Al learning model and the second usage informationof the Al-enabled device; cache the first information associated withthe determined type of service from a cloud server to a local sub-cachein the dedicated cache storage of the memory, wherein the firstinformation of the determined type of service is adaptively cachedduring at least one of a background activity or a foreground activity ofthe Al-enabled device, based on offline state or an online state,specified in the network connectivity information, of the Al-enableddevice, and the first information is cached based on a mapping of thedetermined type of service to a corresponding functional service on thecloud server; and control, based on a user input, delivery of userconsumable information, associated with at least one of the plurality ofintelligent services, on the Al-enabled device, based on at least one ofthe local sub-cache or supplemental information retrievable from thecloud server, wherein the user consumable information is delivered witha maximum dependency on the local sub-cache and a minimum dependency onthe supplemental information.
 2. The Al-enabled device according toclaim 1, wherein the neural circuitry is further configured to train anadaptive machine learning model on the set of user preferences, thenetwork connectivity information of the Al-enabled device, the firstusage information of the plurality of intelligent services, and thesecond usage information of the Al-enabled device, wherein the local Allearning model is generated further based on the trained adaptivemachine learning model.
 3. The Al-enabled device according to claim 1,wherein the neural circuitry is further configured to update the localAl learning model based on one of a real time or a near-real time changein a user activity on the Al-enabled device, wherein each of the set ofuser preferences, the first usage information, and the second usageinformation selectively changes with a change in the user activity. 4.The Al-enabled device according to claim 3, wherein the neural circuitryis further configured to determine whether to retrieve the supplementalinformation from on the cloud server, to update the supplementalinformation in the local sub-cache.
 5. The Al-enabled device accordingto claim 1, wherein the local Al learning model is generated based on atleast one of a machine learning model, a deep learning model, or aBayesian model.
 6. The Al-enabled device according to claim 1, whereinthe neural circuitry is further configured to: determine a requirementfor the supplemental information that is utilized to deliver the userconsumable information; selectively retrieve the supplementalinformation from the cloud server via a communication network, based onthe determined requirement for the supplemental information in theonline state; and update the first information in the local sub-cachewith the retrieved supplemental information.
 7. The Al-enabled deviceaccording to claim 1, wherein the neural circuitry is further configuredto generate a local network among the Al-enabled device and a pluralityof secondary devices, wherein the generated local network is at leastone of a wireless home network, a wireless local area network, or awireless ad hoc network, and the Al-enabled device acts as an accesspoint for the plurality of secondary devices.
 8. The Al-enabled deviceaccording to claim 7, wherein the neural circuitry is further configuredto distribute the local sub-cache in the Al-enabled device among theplurality of secondary devices, via the generated local network, basedon a device specification that indicates capability of the plurality ofsecondary devices, wherein the local sub-cache is distributed to a localstorage on the plurality of secondary devices.
 9. The Al-enabled deviceaccording to claim 7, wherein the neural circuitry is further configuredto transfer the generated local Al learning model among the plurality ofsecondary devices, via the generated local network, wherein the local Allearning model is transferred to a local storage on the plurality ofsecondary devices.
 10. The Al-enabled device according to claim 7,wherein the neural circuitry is further configured to deliver the userconsumable information to at least one secondary devices of theplurality of secondary devices connected to each other via the generatedlocal network.
 11. The Al-enabled device according to claim 10, whereinthe user consumable information is delivered, based on the localsub-cache on the plurality of secondary devices and computationalresources of the plurality of secondary devices.
 12. The Al-enableddevice according to claim 1, wherein the neural circuitry is furtherconfigured to identify the local sub-cache that comprises the firstinformation, into user-sensitive information and public information. 13.The Al-enabled device according to claim 12, wherein the neuralcircuitry is further configured to assign a security privilege level tothe user-sensitive information from the local sub-cache of the type ofservice, wherein the security privilege level is assigned based onpre-specified sensitivity information and a set of user-specifiedpermissions.
 14. The Al-enabled device according to claim 12, whereinthe neural circuitry is further configured to share the user-sensitiveinformation with a plurality of secondary devices connected via a localnetwork, with the Al-enabled device, wherein the user-sensitiveinformation is shared based on authentication of the plurality ofsecondary devices for the assigned security privilege level on theuser-sensitive information.
 15. The Al-enabled device according to claim1, wherein the user consumable information associated with the pluralityof intelligent services comprises at least one of audio content, videocontent, text content, image, graphics, or audio-visual notifications.16. The Al-enabled device according to claim 1, wherein the maximumdependency on the local sub-cache further minimizes a response timebetween a request to deliver the user consumable information and adelivery time of the user consumable information at the Al-enableddevice.
 17. The Al-enabled device according to claim 1, wherein the userconsumable information is delivered with a maximum dependency on thelocal sub-cache and a zero dependency on the supplemental information,in the offline state of the Al-enabled device.
 18. The Al-enabled deviceaccording to claim 1, wherein the user consumable information is furtherdelivered based on a request received from at least one of theAl-enabled device or the cloud server, and wherein the request is atleast one of a user request, a device request, a request initiated by atleast one intelligent service of the plurality of intelligent services,or the functional service on the cloud server.
 19. A method, comprising:in an artificial intelligence (Al)-enabled device that comprises amemory and neural circuitry that handles operations of a plurality ofintelligent services on the Al-enabled device: storing, by the memory,instructions associated with a plurality of intelligent services thatoperates in the Al-enabled device, a set of user preferences, networkconnectivity information of the Al-enabled device, first usageinformation of the plurality of intelligent services, and second usageinformation of the Al-enabled device; allocating, by the neuralcircuitry, a dedicated cache storage for the plurality of intelligentservices; generating, by the neural circuitry, a local Al learning modelassociated with the Al-enabled device, wherein the local Al learningmodel is generated based on the set of user preferences, the networkconnectivity information of the Al-enabled device, and the first usageinformation of the plurality of intelligent services; determining, bythe neural circuitry, a type of service and first information, whereinthe type of service is associated with at least one of the plurality ofintelligent services, the first information is associated with thedetermined type of service, the first information cached at thededicated cache storage, based on the generated local Al learning modeland the second usage information of the Al-enabled device; caching, bythe neural circuitry, the first information associated with thedetermined type of service, from a cloud server, to a local sub-cache inthe dedicated cache storage of the memory, wherein the first informationof the determined type of service is adaptively cached during at leastone of a background activity or a foreground activity of the Al-enableddevice, based on an offline state or an online state, specified in thenetwork connectivity information, of the Al-enabled device, and thefirst information is cached based on a mapping of the determined type ofservice to a corresponding functional service on the cloud server; andcontrolling, by the neural circuitry, based on a user input, delivery ofuser consumable information, associated with at least one of theplurality of intelligent services, on the Al-enabled device, based on atleast one of the local sub-cache or supplemental information retrievablefrom the cloud server, wherein the user consumable information isdelivered with a maximum dependency on the local sub-cache and a minimumdependency on the supplemental information.
 20. The method according toclaim 19, further comprising training by the neural circuitry, anadaptive machine learning model on the set of user preferences, thenetwork connectivity information of the Al-enabled device, the firstusage information of the plurality of intelligent services, and thesecond usage information of the Al-enabled device, wherein the local Allearning model is generated further based on the trained adaptivemachine learning model.